This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

To cope with the competitive worldwide marketplace, organizations rely on business intelligence to an increasing extent. Cyber security is an inevitable practice to protect the entire business sector and its customer. This book presents the significance and application of cyber security for safeguarding organizations, individuals’ personal information, and government. The book provides both practical and managerial implications of cyber security that also supports business intelligence and discusses the latest innovations in cyber security. It offers a roadmap to master degree students and PhD researchers for cyber security analysis in order to minimize the cyber security risk and protect customers from cyber-attack. The book also introduces the most advanced and novel machine learning techniques including, but not limited to, Support Vector Machine, Neural Networks, Extreme Learning Machine, Ensemble Learning, and Deep Learning Approaches, with a goal to apply those to cyber risk management datasets. It will also leverage real-world financial instances to practise business product modelling and data analysis. The contents of this book will be useful for a wide audience who are involved in managing network systems, data security, data forecasting, cyber risk modelling, fraudulent credit risk detection, portfolio management, and data regulatory bodies. It will be particularly beneficial to academics as well as practitioners who are looking to protect their IT system, and reduce data breaches and cyber-attack vulnerabilities.

In an age defined by unparalleled technological advancements, globalization, and the looming specter of environmental and societal crises, the need for a holistic and sustainable approach to accounting practices has never been more pressing. Academic scholars stand witness to the challenges posed by the new era, characterized by transformative shifts across industry, education, community, and society at large. These shifts, driven by rapid advancements in Artificial Intelligence (AI), present a double-edged sword. While AI offers unprecedented opportunities for innovation, it also amplifies the urgency of addressing sustainability concerns. Today's society grapples with the immense responsibility of achieving the Sustainable Development Goals (SDGs) outlined in Agenda 2030. It is imperative to not only understand but harness the power of AI to drive sustainability, enhance the quality of life, and ensure sustainable growth on both local and global scales. Artificial Intelligence Approaches to Sustainable Accounting serves as a beacon of knowledge, providing a comprehensive exploration of the intersection between AI, accounting, and sustainability. This book represents a vital solution to the challenges faced by academic scholars and practitioners alike. Within its pages lies a transdisciplinary approach that bridges the gap between these critical fields. Discover how AI can elevate accounting to new heights, extending the spectrum of information in organizational decision-making, promoting responsible reporting practices, and bolstering sustainable practices worldwide. This book not only reviews governance and management processes but also offers practical methodologies that empower organizations to embrace sustainability wholeheartedly.

Businesses must confront a pressing challenge—how to navigate the turbulent seas of market dynamics and technological advancements. As traditional business models face obsolescence in the wake of relentless innovation, enterprises failing to quickly to harness the power of advanced technologies risk fading into obscurity. This predicament sets the stage for a critical exploration of the profound impact of artificial intelligence (AI) and smart technology on the sustainability of entrepreneurial ventures. Without a strategic embrace of these innovations, businesses find themselves adrift, struggling to cope with the demands of a rapidly evolving market. Utilizing AI and Smart Technology to Improve Sustainability in Entrepreneurship serves as a guide for entrepreneurs seeking to thrive in this era of unprecedented change. Readers begin with a deep dive into the challenges faced by contemporary businesses. This book meticulously dissects these challenges and provides guidance for those ready to chart a course toward sustainable success by leveraging the transformative and compelling aptitudes of AI. The proposed solution of the integration of AI and smart technologies into the business landscape is one of great promise.

Future-Proof Accounting: Data and Technology Strategies equips accounting students, professors, and industry experts with the knowledge needed to navigate the dynamic realm of accounting.

This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes.

In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics. Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling. With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields. Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.